import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
		__a    :	Tuple         =    argparse.ArgumentParser(
		    description=(
		        """Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned"""
		        """ Distillation"""
		    )
		)
		parser.add_argument("""--model_type""", default="""roberta""", choices=["""roberta""", """gpt2"""])
		parser.add_argument("""--model_name""", default="""roberta-large""", type=str)
		parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_roberta_048131723.pth""", type=str)
		parser.add_argument("""--vocab_transform""", action="""store_true""")
		__a    :	List[Any]         =    parser.parse_args()

		if args.model_type == "roberta":
				__a    :	Tuple         =    RobertaForMaskedLM.from_pretrained(args.model_name)
				__a    :	Tuple         =    """roberta"""
		elif args.model_type == "gpt2":
				__a    :	str         =    GPTaLMHeadModel.from_pretrained(args.model_name)
				__a    :	Any         =    """transformer"""

		__a    :	List[Any]         =    model.state_dict()
		__a    :	Dict         =    {}

		# Embeddings #
		if args.model_type == "gpt2":
				for param_name in ["wte.weight", "wpe.weight"]:
						__a    :	Optional[int]         =    state_dict[F'''{prefix}.{param_name}''']
		else:
				for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
						__a    :	List[str]         =    F'''{prefix}.embeddings.{w}.weight'''
						__a    :	Dict         =    state_dict[param_name]
				for w in ["weight", "bias"]:
						__a    :	List[str]         =    F'''{prefix}.embeddings.LayerNorm.{w}'''
						__a    :	List[str]         =    state_dict[param_name]

    # Transformer Blocks #
		__a    :	Optional[int]         =    0
		for teacher_idx in [0, 2, 4, 7, 9, 1_1]:
				if args.model_type == "gpt2":
						for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
								for w in ["weight", "bias"]:
										__a    :	Union[str, Any]         =    state_dict[
										    F'''{prefix}.h.{teacher_idx}.{layer}.{w}'''
										]
						__a    :	str         =    state_dict[F'''{prefix}.h.{teacher_idx}.attn.bias''']
				else:
						for layer in [
						    "attention.self.query",
						    "attention.self.key",
						    "attention.self.value",
						    "attention.output.dense",
						    "attention.output.LayerNorm",
						    "intermediate.dense",
						    "output.dense",
						    "output.LayerNorm",
						]:
								for w in ["weight", "bias"]:
										__a    :	Any         =    state_dict[
										    F'''{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}'''
										]
				std_idx += 1

		# Language Modeling Head ###s
		if args.model_type == "roberta":
				for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
						__a    :	Dict         =    state_dict[F'''{layer}''']
				if args.vocab_transform:
						for w in ["weight", "bias"]:
								__a    :	str         =    state_dict[F'''lm_head.dense.{w}''']
								__a    :	List[Any]         =    state_dict[F'''lm_head.layer_norm.{w}''']
		elif args.model_type == "gpt2":
				for w in ["weight", "bias"]:
						__a    :	Any         =    state_dict[F'''{prefix}.ln_f.{w}''']
				__a    :	Dict         =    state_dict["""lm_head.weight"""]

		print(F'''N layers selected for distillation: {std_idx}''')
		print(F'''Number of params transferred for distillation: {len(compressed_sd.keys())}''')

		print(F'''Save transferred checkpoint to {args.dump_checkpoint}.''')
		torch.save(compressed_sd, args.dump_checkpoint)
import datasets


__a    :	Optional[Any]         =    """\
@InProceedings{conneau2018xnli,
  author = \"Conneau, Alexis
                 and Rinott, Ruty
                 and Lample, Guillaume
                 and Williams, Adina
                 and Bowman, Samuel R.
                 and Schwenk, Holger
                 and Stoyanov, Veselin\",
  title = \"XNLI: Evaluating Cross-lingual Sentence Representations\",
  booktitle = \"Proceedings of the 2018 Conference on Empirical Methods
               in Natural Language Processing\",
  year = \"2018\",
  publisher = \"Association for Computational Linguistics\",
  location = \"Brussels, Belgium\",
}
"""

__a    :	Optional[Any]         =    """\
XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).
"""

__a    :	str         =    """
Computes XNLI score which is just simple accuracy.
Args:
    predictions: Predicted labels.
    references: Ground truth labels.
Returns:
    'accuracy': accuracy
Examples:

    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> xnli_metric = datasets.load_metric(\"xnli\")
    >>> results = xnli_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}
"""





def 							UpperCAmelCase		(       lowercase				,     lowercase				):







				"""simple docstring"""



				return (preds == labels).mean()




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,_KWARGS_DESCRIPTION					)
class   _UpperCamelCase (		datasets.Metric					):


			"""simple docstring"""






			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   str:

							'''simple docstring'''


							return datasets.MetricInfo(
							    description=_DESCRIPTION ,  citation=_CITATION ,  inputs_description=_KWARGS_DESCRIPTION ,  features=datasets.Features(
							        {
							            '''predictions''': datasets.Value('''int64''' if self.config_name != '''sts-b''' else '''float32'''		),
							            '''references''': datasets.Value('''int64''' if self.config_name != '''sts-b''' else '''float32'''		),
							        }		) ,  codebase_urls=[] ,  reference_urls=[] ,  format='''numpy''' ,  )





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   Dict:

							'''simple docstring'''


							return {"accuracy": simple_accuracy(lowerCAmelCase__ ,  lowerCAmelCase__		)}